fp16 = dict(loss_scale=512.0)
model = dict(
    type='CascadeRCNN_Res',
    pretrained='open-mmlab://resnest50',
    backbone=dict(
        type='ResNeSt',
        depth=50,
        num_stages=4,
        in_channels=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=-1,
        norm_cfg=dict(type='SyncBN', requires_grad=True),
        norm_eval=False,
        style='pytorch',
        with_cp = True,
        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
        stage_with_dcn=(False, True, True, True),
        stem_channels=64,
        radix=2,
        reduction_factor=4,
        avg_down_stride=True),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        num_outs=5),
    rpn_head=dict(
        type='RPNHead',
        in_channels=257,
        feat_channels=257,
        anchor_generator=dict(
            type='AnchorGenerator',
            scales=[2, 8],  # 8
            ratios=[ 0.2, 0.5, 1.0, 2.0, 5.0],   #[0.1, 0.3, 0.5, 1.0, 2.0, 5.0, 10.0]
            strides=[4, 8, 16, 32, 64]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[0.0, 0.0, 0.0, 0.0],
            target_stds=[1.0, 1.0, 1.0, 1.0]),
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(
            type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0)),
    roi_head=dict(
        type='CascadeRoIHead',
        num_stages=3,
        stage_loss_weights=[1, 0.5, 0.25],
        bbox_roi_extractor=dict(
            type='SingleRoIExtractor',
            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
            out_channels=257,
            featmap_strides=[4, 8, 16, 32]),
        bbox_head=[
            dict(
                type='Shared4Conv1FCBBoxHead',
                in_channels=257,
                conv_out_channels=256,
                fc_out_channels=1024,
                norm_cfg=dict(type='SyncBN', requires_grad=True),
                roi_feat_size=7,
                num_classes=8,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    target_means=[0.0, 0.0, 0.0, 0.0],
                    target_stds=[0.1, 0.1, 0.2, 0.2]),
                reg_class_agnostic=True,
                loss_cls=dict(
                    type='CrossEntropyLoss',
                    use_sigmoid=False,
                    loss_weight=1.0),
                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
                               loss_weight=1.0)),
            dict(
                type='Shared4Conv1FCBBoxHead',
                in_channels=257,
                conv_out_channels=256,
                fc_out_channels=1024,
                norm_cfg=dict(type='SyncBN', requires_grad=True),
                roi_feat_size=7,
                num_classes=8,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    target_means=[0.0, 0.0, 0.0, 0.0],
                    target_stds=[0.05, 0.05, 0.1, 0.1]),
                reg_class_agnostic=True,
                loss_cls=dict(
                    type='CrossEntropyLoss',
                    use_sigmoid=False,
                    loss_weight=1.0),
                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
                               loss_weight=1.0)),
            dict(
                type='Shared4Conv1FCBBoxHead',
                in_channels=257,
                conv_out_channels=256,
                fc_out_channels=1024,
                norm_cfg=dict(type='SyncBN', requires_grad=True),
                roi_feat_size=7,
                num_classes=8,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    target_means=[0.0, 0.0, 0.0, 0.0],
                    target_stds=[0.033, 0.033, 0.067, 0.067]),
                reg_class_agnostic=True,
                loss_cls=dict(
                    type='CrossEntropyLoss',
                    use_sigmoid=False,
                    loss_weight=1.0),
                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
        ]) )

test_cfg=dict(
        rpn=dict(
            nms_across_levels=False,
            nms_pre=1000,
            nms_post=1000,
            max_num=1000,
            nms_thr=0.5,
            min_bbox_size=0),
        rcnn=dict(
            score_thr=0.0,
            nms=dict(type='nms', iou_threshold=0.4),
            max_per_img=100))
train_cfg=dict(
        rpn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.6,
                neg_iou_thr=0.2,
                min_pos_iou=0.2,
                match_low_quality=True,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=256,
                pos_fraction=0.5,
                neg_pos_ub=-1,
                add_gt_as_proposals=False),
            allowed_border=0,
            pos_weight=-1,
            debug=False),
        rpn_proposal=dict(
            nms_across_levels=False,
            nms_pre=2000,
            nms_post=2000,
            max_num=2000,
            nms_thr=0.7,
            min_bbox_size=0),
        rcnn=[
            dict(
                assigner=dict(
                    type='MaxIoUAssigner',
                    pos_iou_thr=0.4,
                    neg_iou_thr=0.4,
                    min_pos_iou=0.4,
                    match_low_quality=False,
                    ignore_iof_thr=-1),
                sampler=dict(
                    type='RandomSampler',
                    num=512,
                    pos_fraction=0.25,
                    neg_pos_ub=-1,
                    add_gt_as_proposals=True),
                pos_weight=-1,
                debug=False),
            dict(
                assigner=dict(
                    type='MaxIoUAssigner',
                    pos_iou_thr=0.5,
                    neg_iou_thr=0.5,
                    min_pos_iou=0.5,
                    match_low_quality=False,
                    ignore_iof_thr=-1),
                sampler=dict(
                    type='RandomSampler',
                    num=512,
                    pos_fraction=0.25,
                    neg_pos_ub=-1,
                    add_gt_as_proposals=True),
                pos_weight=-1,
                debug=False),
            dict(
                assigner=dict(
                    type='MaxIoUAssigner',
                    pos_iou_thr=0.6,
                    neg_iou_thr=0.6,
                    min_pos_iou=0.6,
                    match_low_quality=False,
                    ignore_iof_thr=-1),
                sampler=dict(
                    type='RandomSampler',
                    num=512,
                    pos_fraction=0.25,
                    neg_pos_ub=-1,
                    add_gt_as_proposals=True),
                pos_weight=-1,
                debug=False)
        ])
dataset_type = 'CzDataset'
data_root = data_root = '/my_data/'
img_norm_cfg = dict(
    mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile', to_float32=True),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Concat', template_path=data_root + 'fast_align_temp/'),
    # dict(type='RandomCrop', crop_size=(2200, 2070)),
    dict(type='Resize',img_scale=[(2131, 1026), (2131, 2060)],multiscale_mode='range',keep_ratio=True),  #(2131, 1026), (2131, 2060)
    dict(type='RandomFlip', flip_ratio=0.5, direction='vertical'),
    dict(type='RandomFlip', flip_ratio=0.5, direction='horizontal'),
    dict(type='BBoxJitter', min=0.9, max=1.1),
    # dict(
    #     type='Normalize',
    #     mean=[123.675, 116.28, 103.53],
    #     std=[58.395, 57.12, 57.375],
    #     to_rgb=True),
    dict(type='Pad', size_divisor=32),
    dict(type='Albu',
         transforms= [dict(type='RandomRotate90', p=0.4)],
         bbox_params=dict(type='BboxParams', format='pascal_voc', label_fields=['gt_labels'],min_visibility=0.0, filter_lost_elements=True),
          keymap=dict(img='image', gt_bboxes='bboxes'),
         update_pad_shape=False,
          skip_img_without_anno=True),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        #scale_factor=1.0,
        img_scale = [(2058, 2131),(1650, 1628), (1023, 1126)],    #(1650,1628),(2058, 2131), (1023, 1126)
        flip=True,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            # dict(
            #     type='Normalize',
            #     mean=[123.675, 116.28, 103.53],
            #     std=[58.395, 57.12, 57.375],
            #     to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ])
]
data = dict(
    samples_per_gpu=1,                          #1
    workers_per_gpu=1,
    train=dict(
         type='RepeatDataset',
         times = 2,                            #2
         dataset=dict(
             type='CzDataset',
             ann_file= data_root + 'annotations_cocoFormat.json',
             img_prefix=  data_root + 'ori_img/',
             pipeline=train_pipeline)),
    test=dict(
        type='CzDataset',
        ann_file='/home/mjw/mmdetection/data/coco/annotations_cocoFormat.json',
        img_prefix='/home/mjw/mmdetection/data/coco/test_imgs/',
        pipeline=test_pipeline))
evaluation = dict(interval=1, metric='bbox')
optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=0.001,
    step=[10, 15])
total_epochs = 20
checkpoint_config = dict(interval=1)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = '/work_dir/pre_trained_weight/coco_pretrained_weights_classes_8_inchannels_4.pth'
resume_from = None
workflow = [('train', 1)]
norm_cfg = dict(type='SyncBN', requires_grad=True)
